Objective
Since the recent years, climate change has become increasingly alarming. The impact of it seems to be an serious issue for both humans and animals. As a result, the analysis in this report is regarding the Co2 emission per country.
Our analysis is developed from variables including country, year, co2, population, GDP, fossil fuel consumption, country size, agriculture production and life expectancy. We obtained the main dataset from Our World in Data website. This dataset includes attributes like country name, year, total Co2 emission, GDP, population and other greenhouse gases.
Since the country size and Fossil Fuel data were not present in the dataset, we scraped it from here and here respectively.
We get the agriculture data for 30 different types of crops from here and combined them together to get the total amount of production each year per country. Later we combined another dataset, Life Expectancy from The World Bank website to get all the potential data which we need to answer our research questions.
Research Questions
How does the ranking of the top 10 countries change in terms of annual CO2 emissions?
Which factors/predictors contribute the most to the top three CO2 emitter countries?
Is there a relationship between co2 emission and country size or life expectancy?
Which country has emitted the most CO2 in total since 1990?
Which country has made the best progress in reducing CO2 emissions during the last decade?
How does CO2 emission of Australia behave over the years compared to top two emitters?
Variable Information and Explanation
| Code |
| country |
| year |
| co2 |
| co2_percap |
| total_production_tone |
| population |
| gdp |
| gdp_percap |
| fos_percap |
| total_size_km2 |
| life_expectancy |
Below is the description of the variables used in the dataset:
Code : ISO 3166-1 alpha-3 – three-letter country codes
country : Geographic location
year : Year of observation
co2 : Annual production-based emissions of carbon dioxide (CO2), measured in million tonnes.
population : Population by country
gdp : Gross domestic product measured in international-$
totalproduction_tone : Total agriculture production in tonnes
total_size_km2 : Country size in km^2
Fos_percap : Fossil fuel consumption per capita
life_expectancy : life expectancies by country over the years
Answering Research Questions 1
This race chart depicts the change in rank of the top 10 Co2 emitters in terms of annual Co2 emission between 1961 and 2018. The United States held the top position from 1961 until 2005, when China surpasses it and retained the top spot till 2018.
India was last in 1961, but in 2009 it rapidly surpassed Russia, which had always been in the top three rankings, and remained in third place until 2018.
Countries such as Canada, Ukraine, and France have consistently been in the bottom three.
Answering Research Questions 2
Answering Research Questions 2
# A tibble: 210 × 4
country cum_co2pc08 inc_0818 pct_inc
<chr> <dbl> <dbl> <dbl>
1 North Korea 67.3 16.8 0.250
2 Nauru 163. 49.5 0.303
3 Moldova 42.2 13.4 0.319
4 Zimbabwe 22.0 7.91 0.360
5 Denmark 211. 80.8 0.382
6 Somalia 1.31 0.525 0.400
7 Gabon 74.1 29.8 0.402
8 North Macedonia 104. 42.4 0.408
9 Syria 57.4 23.5 0.410
10 Qatar 1067. 442. 0.414
# … with 200 more rows
Answering Research Questions 3
This line graph shows co2 emission in Australia, United States and China over the years 1850 - 2018.
The graph clearly depicts that China has the highest co2 emission compared to Australia and US. This is mainly due to the fact that higher standards of living, comparatively fossil-intensive electric power, and its role as the manufacturer of goods consumed around the world.
The highest co2 emission was recorded in the year 2018 whereas the lowest co2 emission was recorded in the year 1960 in Australia.
The highest amount of co2 emission was recoreded in the past decade compared to the other years.
Co2 emission gradually started changing after the year 1850 along with the industrial revolution but a clear peak can be observed after the year 1950.
Australia has a more flatter curve compared to other 2 countries in the recent years and the main cause for this is decrease in transport emissions due to COVID-19 restrictions, reduced fugitive emissions, and reductions in emissions from electricity.
There are some fluctuations in United States Co2 emission over the years 1900 and 1950 which is maily due to the fossil fuels that people are burning for energy.
Answering Research Questions 3
This line graphs shows how co2 emission per person behaivour over the years 1961 - 2018 in Australia, United States and China.
The highest co2 emission country was United States over the year compared to other 2 countries. The line graph clearly shows fluctuations between years 1900 and 1950 for United States. This was mainly due to burning of fossil fuels, increased electricla consumption in domestics and manufacturing in vehicles.
The co2 per capita emission started to decline since 2004. This may be due to the fact that Australian government has put more attention on climate change issue and they may have encourage clean energy generation such as solar.
After year 1900 the trend starts to steeping this is because of country was developing at that period of time.
One of the main observations is that China has started co2 emission per person after the 1900.
Compared to China,co2 emission of Australia and US have dropped over the past few years this is due to coal to gas switching in the power sector and by people, reduced electricity use and changes in transport emissions.
As United States and China are among the most populative countries the co2 per capita emission is always high compared to Australia.
Answering Is there a relationship between co2 emission and country size or life expectancy?
Findings
Findings
Call:
lm(formula = co2 ~ total_size_km2 + population + total_production_tone +
gdp + tot_fos, data = dt1)
Residuals:
Min 1Q Median 3Q Max
-246.69 -18.01 10.80 35.67 189.88
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.114e+00 1.116e+01 -0.817 0.4168
total_size_km2 -2.225e-02 4.763e-03 -4.670 1.36e-05 ***
population 5.749e-01 8.849e-02 6.496 9.25e-09 ***
total_production_tone 2.251e-01 9.636e-02 2.336 0.0223 *
gdp -1.248e-01 1.341e-02 -9.303 5.70e-14 ***
tot_fos 3.423e-04 8.472e-06 40.400 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 83.65 on 72 degrees of freedom
Multiple R-squared: 0.9961, Adjusted R-squared: 0.9958
F-statistic: 3695 on 5 and 72 DF, p-value: < 2.2e-16
Findings
Findings from the graph
Answering Which factors/predictors contribute the most to United States?
Call:
lm(formula = co2_percap ~ gdp_percap + production_percap + life_expectancy +
fos_percap, data = dt2)
Residuals:
Min 1Q Median 3Q Max
-1.8085 -0.4265 -0.0312 0.3221 2.6094
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.463e+01 1.192e+01 -2.906 0.00502 **
gdp_percap -9.235e-04 4.400e-02 -0.021 0.98332
production_percap 7.864e-01 3.714e-01 2.118 0.03810 *
life_expectancy 3.398e-01 1.843e-01 1.844 0.06984 .
fos_percap 3.354e-01 2.041e-02 16.434 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8229 on 64 degrees of freedom
Multiple R-squared: 0.8598, Adjusted R-squared: 0.8511
F-statistic: 98.14 on 4 and 64 DF, p-value: < 2.2e-16
Call:
lm(formula = co2_percap ~ gdp_percap + I(gdp_percap^2) + production_percap +
life_expectancy + fos_percap, data = dt2)
Residuals:
Min 1Q Median 3Q Max
-0.97917 -0.28180 -0.01885 0.29253 1.39405
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.6611987 9.3140647 2.540 0.013551 *
gdp_percap 0.8007074 0.0829754 9.650 4.94e-14 ***
I(gdp_percap^2) -0.0083007 0.0008117 -10.226 5.20e-15 ***
production_percap 0.4028620 0.2325611 1.732 0.088117 .
life_expectancy -0.5456318 0.1430742 -3.814 0.000314 ***
fos_percap 0.2329062 0.0161129 14.455 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5085 on 63 degrees of freedom
Multiple R-squared: 0.9473, Adjusted R-squared: 0.9431
F-statistic: 226.5 on 5 and 63 DF, p-value: < 2.2e-16
Answering Which factors/predictors contribute the most to China?
Call:
lm(formula = co2_percap ~ gdp_percap + production_percap + life_expectancy +
fos_percap, data = dt3)
Residuals:
Min 1Q Median 3Q Max
-0.34317 -0.13249 -0.00357 0.07547 0.75826
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.677e-01 2.742e-01 2.071 0.0424 *
gdp_percap 3.257e-05 5.858e-05 0.556 0.5802
production_percap 5.959e-01 4.801e-01 1.241 0.2191
life_expectancy -1.442e-02 8.995e-03 -1.603 0.1139
fos_percap 2.900e-04 3.072e-05 9.441 9.67e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1931 on 64 degrees of freedom
Multiple R-squared: 0.9922, Adjusted R-squared: 0.9918
F-statistic: 2045 on 4 and 64 DF, p-value: < 2.2e-16
# A tibble: 1 × 1
correlation
<dbl>
1 0.996
Answering Which factors/predictors contribute the most to Russia?
Call:
lm(formula = co2_percap ~ gdp_percap + production_percap + life_expectancy +
fos_percap, data = dt4)
Residuals:
Min 1Q Median 3Q Max
-4.3383 -1.1504 -0.3295 0.8218 3.6094
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.415e+00 1.292e+01 0.419 0.677
gdp_percap 2.935e-04 4.952e-05 5.927 1.35e-07 ***
production_percap -5.632e+00 1.059e+00 -5.318 1.42e-06 ***
life_expectancy -1.242e-01 2.272e-01 -0.547 0.586
fos_percap 3.964e-04 5.048e-05 7.853 5.87e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.805 on 64 degrees of freedom
Multiple R-squared: 0.7209, Adjusted R-squared: 0.7035
F-statistic: 41.34 on 4 and 64 DF, p-value: < 2.2e-16
Call:
lm(formula = co2_percap ~ gdp_percap + I(gdp_percap^2) + production_percap +
life_expectancy + fos_percap, data = dt4)
Residuals:
Min 1Q Median 3Q Max
-1.6875 -0.8740 -0.1250 0.6935 2.5793
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.238e+01 9.435e+00 -4.492 3.07e-05 ***
gdp_percap 1.686e-03 1.434e-04 11.760 < 2e-16 ***
I(gdp_percap^2) -5.451e-08 5.478e-09 -9.950 1.52e-14 ***
production_percap -1.253e+00 7.980e-01 -1.570 0.12131
life_expectancy 5.220e-01 1.569e-01 3.328 0.00146 **
fos_percap 1.981e-04 3.747e-05 5.286 1.66e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.135 on 63 degrees of freedom
Multiple R-squared: 0.8915, Adjusted R-squared: 0.8829
F-statistic: 103.5 on 5 and 63 DF, p-value: < 2.2e-16
Analysis of Variance Table
Model 1: co2_percap ~ gdp_percap + production_percap + life_expectancy +
fos_percap
Model 2: co2_percap ~ gdp_percap + I(gdp_percap^2) + production_percap +
life_expectancy + fos_percap
Res.Df RSS Df Sum of Sq F Pr(>F)
1 64 208.555
2 63 81.104 1 127.45 99.002 1.523e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Answering Are they the same factor that contribute the most to co2 emission of each countries?
Finding: fossil fuel consumption is an important driver of co2 emission for all the countries
Call:
lm(formula = co2_percap ~ gdp_percap + I(gdp_percap^2) + production_percap +
life_expectancy + fos_percap, data = dt2)
Residuals:
Min 1Q Median 3Q Max
-0.97917 -0.28180 -0.01885 0.29253 1.39405
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.6611987 9.3140647 2.540 0.013551 *
gdp_percap 0.8007074 0.0829754 9.650 4.94e-14 ***
I(gdp_percap^2) -0.0083007 0.0008117 -10.226 5.20e-15 ***
production_percap 0.4028620 0.2325611 1.732 0.088117 .
life_expectancy -0.5456318 0.1430742 -3.814 0.000314 ***
fos_percap 0.2329062 0.0161129 14.455 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5085 on 63 degrees of freedom
Multiple R-squared: 0.9473, Adjusted R-squared: 0.9431
F-statistic: 226.5 on 5 and 63 DF, p-value: < 2.2e-16
Call:
lm(formula = co2_percap ~ gdp_percap + production_percap + life_expectancy +
fos_percap, data = dt3)
Residuals:
Min 1Q Median 3Q Max
-0.34317 -0.13249 -0.00357 0.07547 0.75826
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.677e-01 2.742e-01 2.071 0.0424 *
gdp_percap 3.257e-05 5.858e-05 0.556 0.5802
production_percap 5.959e-01 4.801e-01 1.241 0.2191
life_expectancy -1.442e-02 8.995e-03 -1.603 0.1139
fos_percap 2.900e-04 3.072e-05 9.441 9.67e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1931 on 64 degrees of freedom
Multiple R-squared: 0.9922, Adjusted R-squared: 0.9918
F-statistic: 2045 on 4 and 64 DF, p-value: < 2.2e-16
Call:
lm(formula = co2_percap ~ gdp_percap + I(gdp_percap^2) + production_percap +
life_expectancy + fos_percap, data = dt4)
Residuals:
Min 1Q Median 3Q Max
-1.6875 -0.8740 -0.1250 0.6935 2.5793
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.238e+01 9.435e+00 -4.492 3.07e-05 ***
gdp_percap 1.686e-03 1.434e-04 11.760 < 2e-16 ***
I(gdp_percap^2) -5.451e-08 5.478e-09 -9.950 1.52e-14 ***
production_percap -1.253e+00 7.980e-01 -1.570 0.12131
life_expectancy 5.220e-01 1.569e-01 3.328 0.00146 **
fos_percap 1.981e-04 3.747e-05 5.286 1.66e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.135 on 63 degrees of freedom
Multiple R-squared: 0.8915, Adjusted R-squared: 0.8829
F-statistic: 103.5 on 5 and 63 DF, p-value: < 2.2e-16
Credits :
Junyan Zhou 29624819
Xueying Li 31964125
Kshitija Hire 31972896
Senath Laksika Ranaweera 31021670